Information and data management systems have been employed in large organizations to effectively maintain data within an organization. Further, data is an asset for organizations and hence it is very important that maintained data is well organized and secured. Presently, organizations manage various kinds of information. Most of the information is primarily related to core businesses and are stored and managed in Enterprise systems, ERPs like SAP, Oracle Apps or Peoplesoft. However, there is a large set of information maintained in adhoc or semi-structured manner that cannot be stored in enterprise systems. Information like Application Inventory, Open Source Software Inventory, Software Asset Inventory, Process Inventory, Known Error Inventory etc., are not stored in any enterprise system.
Existing approach of solving the problem is to only create a custom repository for storing such information.
Present day organizations are distributed in nature. The organizations usually operate out of multiple departments and offices that are geographically distributed. Further, the data that is maintained within such an organization is also distributed across various departments, business units and facilities. The data management systems employed in such organizations manage information that is present locally within the departments, business units and so on within the organization. Further, existing systems do not provide any means to configure the data that is maintained within the organization. Due to this drawback, every time a user wants some information from the system he needs to build a custom solution each time for his requirement as there is no provision for configuring the data. The organizations are is compelled to create a central repository to store the information from different departments, business units and so on at one of the locations so as to suit its operational requirements. This process of customizing the solution is time consuming and cumbersome.
The search mechanisms offered by these systems are not precise due to unorganized and unrelated state of information, hence cumbersome and increases search time of users. Moreover, these systems generally follow directory based approach and do not facilitate the keyword based search to retrieve the relevant data required by the user. Further, there are no analytics in the data captured by such legacy search tools. As a result, there may be a lot of repeated work and wastage of effort of the user in conducting the search. Further, these systems have poor reporting features wherein the searched data cannot be exported to a user desired format. Also, existing search mechanisms do not provide options to the user to drill down or browse through the search results. Due to the aforementioned drawbacks, the data access process becomes time consuming and cumbersome.
Systems that can create data catalogs do not provide the feature to configure the created data catalogs and customize the same as per the requirements of the user. Customization of data catalogs, in absence of ready to use feature, consumes a lot of time and resources. In addition, the Metadata associated with the catalog cannot be customized to create custom attributes for the entity to be stored in the catalog. Moreover, it is practically not possible to create multiple catalogs for different entities and subsequently to establish multiple associations there between the entities across the organization.
Further, the existing cataloguing systems do not offer a flexibility to slice and dice the data as per the requirements of the user. In addition, these systems do not provide the option of clustering and segmenting the data in order to perform data analysis in a strategic manner. The existing cataloguing systems lack the ability to incorporate new attributes of applications, which are required for carrying out detailed portfolio analysis of the data and are unable to adapt to the updates made on the data structure of the catalog. Also, addition of new attributes by the architects and service managers at any point of time is not possible in the existing systems. Hence, when a new application is to be developed, the user cannot employ the stored data and modify the same to suit his/her requirements. In absence of a feature for data analysis and exploitation, the user needs to write codes and develop tool from the scratch for gaining a limited configurability of the system. Thus, existing cataloguing systems, seldom encourages users to use pre-available features to exploit data and instead necessitate user to develop personalized tools which is monetarily taxing and time consuming.
Due to the aforementioned drawbacks it is evident that existing data management systems and catalog builder tools are not effective due to high cost, lack of user friendliness and flexibility of operations.